From c7569c30428cdafddde9c8916ed3a228eb1cbb56 Mon Sep 17 00:00:00 2001 From: Avik Basu <3485425+ab93@users.noreply.github.com> Date: Fri, 23 Feb 2024 15:07:58 -0800 Subject: [PATCH] feat: allow chunked druid fetch (#349) Client side fetch time comparision: - Normal fetch, 72 hours: `0.35 s` - Chunked fetch, 72 hours, 6 hour chunk: `0.34 s` --------- Signed-off-by: Avik Basu Signed-off-by: skondakindi --- numalogic/connectors/druid/_druid.py | 186 +- poetry.lock | 1427 +++++++------- pyproject.toml | 6 +- tests/connectors/test_druid.py | 355 ++-- tests/connectors/test_prometheus.py | 6 - tests/resources/data/raw_druid.csv | 2626 ++++++++++++++++++++++++++ 6 files changed, 3660 insertions(+), 946 deletions(-) create mode 100644 tests/resources/data/raw_druid.csv diff --git a/numalogic/connectors/druid/_druid.py b/numalogic/connectors/druid/_druid.py index 0345621a..c62d4fec 100644 --- a/numalogic/connectors/druid/_druid.py +++ b/numalogic/connectors/druid/_druid.py @@ -1,5 +1,6 @@ import logging import time +from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timedelta import pandas as pd @@ -10,12 +11,13 @@ from numalogic.connectors._base import DataFetcher from numalogic.connectors._config import Pivot -from typing import Optional +from typing import Optional, Final from numalogic.tools.exceptions import DruidFetcherError _LOGGER = logging.getLogger(__name__) -TIMEOUT = 10000 +TIMEOUT: Final[int] = 10000 +_MAX_CONCURRENCY: Final[int] = 16 # TODO: pass dictionary of keys and values as dict @@ -41,6 +43,7 @@ def build_params( delay: float, aggregations: Optional[list[str]] = None, post_aggregations: Optional[list[str]] = None, + reference_dt: Optional[datetime] = None, ) -> dict: """ @@ -56,6 +59,8 @@ def build_params( ``pydruid.utils.aggregators`` e.g., ``doublesum`` post_aggregations: A map from post aggregator name to one of the ``pydruid.utils.postaggregator`` e.g., ``QuantilesDoublesSketchToQuantile``. + reference_dt: reference datetime to calculate start and end dt + (None will mean using current datetime). Returns: a dict of parameters @@ -64,7 +69,8 @@ def build_params( type="and", fields=[Filter(type="selector", dimension=k, value=v) for k, v in filter_pairs.items()], ) - end_dt = datetime.now(pytz.utc) - timedelta(hours=delay) + reference_dt = reference_dt or datetime.now(pytz.utc) + end_dt = reference_dt - timedelta(hours=delay) _LOGGER.debug("Querying with end_dt: %s, that is with delay of %s hrs", end_dt, delay) start_dt = end_dt - timedelta(hours=hours) @@ -98,6 +104,8 @@ class DruidFetcher(DataFetcher): """ + __slots__ = ("client",) + def __init__(self, url: str, endpoint: str): super().__init__(url) self.client = PyDruid(url, endpoint) @@ -116,6 +124,28 @@ def fetch( pivot: Optional[Pivot] = None, hours: float = 24, ) -> pd.DataFrame: + """ + Fetch data from Druid. + + Args: + ------ + datasource: Data source to query + filter_keys: keys + filter_values: values + dimensions: The dimensions to group by + delay: Added delay to the fetch query from current time. + granularity: Time bucket to aggregate data by hour, day, minute, etc. + aggregations: A map from aggregator name to one of the + ``pydruid.utils.aggregators`` e.g., ``doublesum`` + post_aggregations: postaggregations map + group_by: List of columns to group by + pivot: Pivot configuration + hours: Hours from now to fetch. + + Returns + ------- + Fetched dataframe + """ _start_time = time.perf_counter() filter_pairs = make_filter_pairs(filter_keys, filter_values) query_params = build_params( @@ -128,33 +158,131 @@ def fetch( aggregations=aggregations, post_aggregations=post_aggregations, ) - try: - response = self.client.groupby(**query_params) - except Exception as err: - raise DruidFetcherError("Druid Exception:\n") from err - else: - df = response.export_pandas() - if df.empty or df.shape[0] == 0: - logging.warning("No data found for keys %s", filter_pairs) - return pd.DataFrame() - - df["timestamp"] = pd.to_datetime(df["timestamp"]).astype("int64") // 10**6 - - if group_by: - df = df.groupby(by=group_by).sum().reset_index() - - if pivot and pivot.columns: - df = df.pivot( - index=pivot.index, - columns=pivot.columns, - values=pivot.value, - ) - df.columns = df.columns.map("{0[1]}".format) - df.reset_index(inplace=True) + df = self._fetch(**query_params) + + if df.empty or df.shape[0] == 0: + logging.warning("No data found for keys %s", filter_pairs) + return pd.DataFrame() + + df["timestamp"] = pd.to_datetime(df["timestamp"]).astype("int64") // 10**6 + + if group_by: + df = df.groupby(by=group_by).sum().reset_index() - _end_time = time.perf_counter() - _start_time - _LOGGER.debug("params: %s latency: %.6fs", query_params, _end_time) - return df + if pivot and pivot.columns: + df = df.pivot( + index=pivot.index, + columns=pivot.columns, + values=pivot.value, + ) + df.columns = df.columns.map("{0[1]}".format) + df.reset_index(inplace=True) + + _end_time = time.perf_counter() - _start_time + _LOGGER.debug("Druid params: %s, fetch time: %.4fs", query_params, _end_time) + return df def raw_fetch(self, *args, **kwargs) -> pd.DataFrame: raise NotImplementedError + + def chunked_fetch( + self, + datasource: str, + filter_keys: list[str], + filter_values: list[str], + dimensions: list[str], + delay: float = 3.0, + granularity: str = "minute", + aggregations: Optional[dict] = None, + post_aggregations: Optional[dict] = None, + group_by: Optional[list[str]] = None, + pivot: Optional[Pivot] = None, + hours: int = 24, + chunked_hours: int = 6, + ) -> pd.DataFrame: + """ + Fetch data concurrently, and concatenate the results. + + Args: + ------ + datasource: Data source to query + filter_keys: keys + filter_values: values + dimensions: The dimensions to group by + delay: Added delay to the fetch query from current time. + granularity: Time bucket to aggregate data by hour, day, minute, etc. + aggregations: A map from aggregator name to one of the + ``pydruid.utils.aggregators`` e.g., ``doublesum`` + post_aggregations: postaggregations map + group_by: List of columns to group by + pivot: Pivot configuration + hours: Hours from now to skip training. + chunked_hours: Hours to fetch in each chunk + + Returns + ------- + Fetched dataframe + + Raises + ------ + ValueError: If chunked_hours is less than 1 + """ + if chunked_hours < 1: + raise ValueError("chunked_hours should be integer and >= 1.") + + _start_time = time.perf_counter() + filter_pairs = make_filter_pairs(filter_keys, filter_values) + + hours_elapsed = 0 + chunked_dfs = [] + qparams = [] + curr_time = datetime.now(pytz.utc) + + while hours_elapsed < hours: + ref_dt = curr_time - timedelta(hours=hours_elapsed) + qparams.append( + build_params( + datasource=datasource, + dimensions=dimensions, + filter_pairs=filter_pairs, + granularity=granularity, + hours=min(chunked_hours, hours - hours_elapsed), + delay=delay, + aggregations=aggregations, + post_aggregations=post_aggregations, + reference_dt=ref_dt, + ) + ) + hours_elapsed += chunked_hours + + max_threads = min(_MAX_CONCURRENCY, len(qparams)) + _LOGGER.debug("Fetching data concurrently with %s threads", max_threads) + with ThreadPoolExecutor(max_workers=max_threads) as executor: + futures = [executor.submit(self._fetch, **params) for params in qparams] + for future in futures: + chunked_dfs.append(future.result()) + + df = pd.concat(chunked_dfs, axis=0, ignore_index=True) + df["timestamp"] = pd.to_datetime(df["timestamp"]).astype("int64") // 10**6 + + if group_by: + df = df.groupby(by=group_by).sum().reset_index() + + if pivot and pivot.columns: + df = df.pivot( + index=pivot.index, + columns=pivot.columns, + values=pivot.value, + ) + df.columns = df.columns.map("{0[1]}".format) + df.reset_index(inplace=True) + + _LOGGER.debug("Fetch time: %.4fs", time.perf_counter() - _start_time) + return df + + def _fetch(self, **query_params) -> pd.DataFrame: + try: + response = self.client.groupby(**query_params) + except Exception as err: + raise DruidFetcherError("Druid Exception:\n") from err + return response.export_pandas() diff --git a/poetry.lock b/poetry.lock index 9f25aad2..9856b09e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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"types-setuptools", "types-six", "types-tabulate"] visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.3.0)"] [[package]] @@ -5005,13 +4921,13 @@ files = [ [[package]] name = "tqdm" -version = "4.66.1" +version = "4.66.2" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" files = [ - {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"}, - {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"}, + {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"}, + {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"}, ] [package.dependencies] @@ -5061,27 +4977,6 @@ build = ["cmake (>=3.20)", "lit"] tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"] tutorials = ["matplotlib", "pandas", "tabulate", "torch"] -[[package]] -name = "typer" -version = "0.9.0" -description = "Typer, build great CLIs. Easy to code. Based on Python type hints." -optional = false -python-versions = ">=3.6" -files = [ - {file = "typer-0.9.0-py3-none-any.whl", hash = "sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee"}, - {file = "typer-0.9.0.tar.gz", hash = "sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2"}, -] - -[package.dependencies] -click = ">=7.1.1,<9.0.0" -typing-extensions = ">=3.7.4.3" - -[package.extras] -all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"] -dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"] -doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"] -test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"] - [[package]] name = "types-python-dateutil" version = "2.8.19.20240106" @@ -5106,13 +5001,13 @@ files = [ [[package]] name = "tzdata" -version = "2023.4" +version = "2024.1" description = "Provider of IANA time zone data" optional = false python-versions = ">=2" files = [ - {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"}, - {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"}, + {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"}, + {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"}, ] [[package]] @@ -5164,13 +5059,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "virtualenv" -version = "20.25.0" +version = "20.25.1" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.25.0-py3-none-any.whl", hash = "sha256:4238949c5ffe6876362d9c0180fc6c3a824a7b12b80604eeb8085f2ed7460de3"}, - {file = "virtualenv-20.25.0.tar.gz", hash = "sha256:bf51c0d9c7dd63ea8e44086fa1e4fb1093a31e963b86959257378aef020e1f1b"}, + {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"}, + {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"}, ] [package.dependencies] @@ -5254,13 +5149,13 @@ watchdog = ["watchdog (>=2.3)"] [[package]] name = "widgetsnbextension" -version = "4.0.9" +version = "4.0.10" description = "Jupyter interactive widgets for Jupyter Notebook" optional = false python-versions = ">=3.7" files = [ - {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"}, - {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"}, + {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"}, + {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"}, ] [[package]] @@ -5401,4 +5296,4 @@ redis = ["redis"] [metadata] lock-version = "2.0" python-versions = ">=3.9, <3.12" -content-hash = "2e3df7175c32848bcff73d421543bc02e8358f10b7392f4be7b9ea25ad099fae" +content-hash = "125085b46ab5da1d21990950c6b4a5655805997dfa89b42a455a4e442301fd06" diff --git a/pyproject.toml b/pyproject.toml index e84fc943..5da47245 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,9 +23,6 @@ repository = "https://github.com/numaproj/numalogic" documentation = "https://numalogic.numaproj.io/" homepage = "https://numalogic.numaproj.io/" -[tool.poetry.scripts] -numalogic ="numalogic.backtest.__main__:app" - [tool.poetry.dependencies] python = ">=3.9, <3.12" numpy = "^1.26" @@ -34,8 +31,6 @@ scikit-learn = "^1.3" omegaconf = "^2.3.0" cachetools = "^5.3.0" orjson = "^3.9" -typer = {version = "^0.9", extras = ["rich"]} -matplotlib = "^3.4.2" pynumaflow = "~0.6" prometheus_client = "^0.18.0" @@ -61,6 +56,7 @@ pytorch-lightning = "^2.0" optional = true [tool.poetry.group.dev.dependencies] +matplotlib = "^3.4.2" black = "^23.0" pytest = "^7.1" pytest-cov = "^4.0" diff --git a/tests/connectors/test_druid.py b/tests/connectors/test_druid.py index 03a52c9a..ffeb4c2f 100644 --- a/tests/connectors/test_druid.py +++ b/tests/connectors/test_druid.py @@ -1,17 +1,20 @@ +import datetime import json import logging -import unittest -import datetime -from unittest.mock import patch, Mock +import os.path +import pandas as pd import pydruid.query +import pytest +from deepdiff import DeepDiff +from freezegun import freeze_time from pydruid.client import PyDruid -from pydruid.utils.dimensions import DimensionSpec from pydruid.utils import aggregators from pydruid.utils import postaggregator +from pydruid.utils.dimensions import DimensionSpec from pydruid.utils.filters import Filter -from deepdiff import DeepDiff +from numalogic._constants import TESTS_DIR from numalogic.connectors._config import Pivot from numalogic.connectors.druid import ( DruidFetcher, @@ -25,154 +28,226 @@ logging.basicConfig(level=logging.DEBUG) -def mock_group_by(*_, **__): - """Mock group by response from druid.""" - result = [ - { - "version": "v1", - "timestamp": "2023-07-11T01:36:00.000Z", - "event": {"count": 5.0, "ciStatus": "success"}, - }, - { - "version": "v1", - "timestamp": "2023-07-11T01:37:00.000Z", - "event": {"count": 1.0, "ciStatus": "success"}, - }, - ] - query = pydruid.query.Query(query_dict={}, query_type="groupBy") - query.parse(json.dumps(result)) - return query - - -def mock_group_by_doubles_sketch(*_, **__): - """Mock group by response for doubles sketch from druid.""" - result = [ - { - "event": { - "agg0": 4, - "assetAlias": "Intuit.identity.authn.signin", - "env": "prod", - "postAgg0": 21988, +@pytest.fixture +def setup(): + end = datetime.datetime.now() + start = end - datetime.timedelta(hours=36) + fetcher = DruidFetcher(url="http://localhost:8888", endpoint="druid/v2/") + return start.timestamp(), end.timestamp(), fetcher + + +@pytest.fixture +def mock_group_by(mocker): + """Creates a Mock for PyDruid's groupby method.""" + + def group_by(*_, **__): + """Mock group by response from druid.""" + result = [ + { + "version": "v1", + "timestamp": "2023-07-11T01:36:00.000Z", + "event": {"count": 5.0, "ciStatus": "success"}, }, - "timestamp": "2023-09-06T07:50:00.000Z", - "version": "v1", - }, - { - "event": { - "agg0": 22, - "assetAlias": "Intuit.identity.authn.signin", - "env": "prod", - "postAgg0": 2237.7999997138977, + { + "version": "v1", + "timestamp": "2023-07-11T01:37:00.000Z", + "event": {"count": 1.0, "ciStatus": "success"}, + }, + ] + query = pydruid.query.Query(query_dict={}, query_type="groupBy") + query.parse(json.dumps(result)) + return query + + mocker.patch.object(PyDruid, "groupby", side_effect=group_by) + + +@pytest.fixture +def mock_group_by_doubles_sketch(mocker): + """Creates a Mock for PyDruid's groupby method for doubles sketch.""" + + def group_by(*_, **__): + """Mock group by response for doubles sketch from druid.""" + result = [ + { + "event": { + "agg0": 4, + "assetAlias": "identity.authn.signin", + "env": "prod", + "postAgg0": 21988, + }, + "timestamp": "2023-09-06T07:50:00.000Z", + "version": "v1", }, - "timestamp": "2023-09-06T07:53:00.000Z", - "version": "v1", + { + "event": { + "agg0": 22, + "assetAlias": "identity.authn.signin", + "env": "prod", + "postAgg0": 2237.7999997138977, + }, + "timestamp": "2023-09-06T07:53:00.000Z", + "version": "v1", + }, + ] + query = pydruid.query.Query(query_dict={}, query_type="groupBy") + query.parse(json.dumps(result)) + return query + + mocker.patch.object(PyDruid, "groupby", side_effect=group_by) + + +def test_fetch(setup, mock_group_by): + start, end, fetcher = setup + _out = fetcher.fetch( + filter_keys=["assetId"], + filter_values=["5984175597303660107"], + dimensions=["ciStatus"], + datasource="tech-ip-customer-interaction-metrics", + aggregations={"count": aggregators.doublesum("count")}, + group_by=["timestamp", "ciStatus"], + hours=2, + pivot=Pivot( + index="timestamp", + columns=["ciStatus"], + value=["count"], + ), + ) + assert (2, 2) == _out.shape + + +def test_fetch_double_sketch(setup, mock_group_by_doubles_sketch): + start, end, druid = setup + _out = druid.fetch( + filter_keys=["assetAlias"], + filter_values=["accounting.core.qbowebapp"], + dimensions=["assetAlias", "env"], + datasource="coredevx-rum-perf-metrics", + aggregations={"agg0": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 256)}, + post_aggregations={ + "postAgg0": _post_agg.QuantilesDoublesSketchToQuantile( + output_name="agg0", field=postaggregator.Field("agg0"), fraction=0.9 + ) + }, + hours=2, + ) + assert (2, 5) == _out.shape + + +def test_build_param(setup): + start, end, druid = setup + filter_pairs = make_filter_pairs(["ciStatus"], ["false"]) + expected = { + "datasource": "foo", + "dimensions": map(lambda d: DimensionSpec(dimension=d, output_name=d), ["bar"]), + "filter": Filter( + type="and", + fields=[Filter(type="selector", dimension="ciStatus", value="false")], + ), + "granularity": "all", + "aggregations": {}, + "post_aggregations": {}, + "intervals": "", + "context": { + "timeout": 10000, + "configIds": list(filter_pairs), + "source": "numalogic", }, - ] - query = pydruid.query.Query(query_dict={}, query_type="groupBy") - query.parse(json.dumps(result)) - return query - - -class TestDruid(unittest.TestCase): - start = None - end = None - prom = None - - @classmethod - def setUpClass(cls) -> None: - end = datetime.datetime.now() - start = end - datetime.timedelta(hours=36) - cls.start = start.timestamp() - cls.end = end.timestamp() - cls.druid = DruidFetcher(url="http://localhost:8888", endpoint="druid/v2/") - - @patch.object(PyDruid, "groupby", Mock(return_value=mock_group_by())) - def test_fetch(self): - _out = self.druid.fetch( + } + + actual = build_params( + datasource="foo", + dimensions=["bar"], + filter_pairs=filter_pairs, + granularity="all", + hours=24.0, + delay=3, + ) + actual["intervals"] = "" + diff = DeepDiff(expected, actual, ignore_order=True) + assert {} == diff + + +def test_fetch_exception(mocker, setup): + start, end, druid = setup + mocker.patch.object(PyDruid, "groupby", side_effect=OSError) + with pytest.raises(DruidFetcherError): + _out = druid.fetch( filter_keys=["assetId"], filter_values=["5984175597303660107"], dimensions=["ciStatus"], - datasource="tech-ip-customer-interaction-metrics", + datasource="customer-interaction-metrics", aggregations={"count": aggregators.doublesum("count")}, group_by=["timestamp", "ciStatus"], - hours=2, + hours=36, pivot=Pivot( index="timestamp", columns=["ciStatus"], value=["count"], ), ) - self.assertEqual((2, 2), _out.shape) - - @patch.object(PyDruid, "groupby", Mock(return_value=mock_group_by_doubles_sketch())) - def test_fetch_double_sketch(self): - _out = self.druid.fetch( - filter_keys=["assetAlias"], - filter_values=["Intuit.accounting.core.qbowebapp"], - dimensions=["assetAlias", "env"], - datasource="coredevx-rum-perf-metrics", - aggregations={ - "agg0": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 256) - }, - post_aggregations={ - "postAgg0": _post_agg.QuantilesDoublesSketchToQuantile( - output_name="agg0", field=postaggregator.Field("agg0"), fraction=0.9 - ) - }, - hours=2, - ) - self.assertEqual((2, 5), _out.shape) - - def test_build_param(self): - filter_pairs = make_filter_pairs(["ciStatus"], ["false"]) - expected = { - "datasource": "foo", - "dimensions": map(lambda d: DimensionSpec(dimension=d, output_name=d), ["bar"]), - "filter": Filter( - type="and", - fields=[Filter(type="selector", dimension="ciStatus", value="false")], - ), - "granularity": "all", - "aggregations": {}, - "post_aggregations": {}, - "intervals": "", - "context": { - "timeout": 10000, - "configIds": list(filter_pairs), - "source": "numalogic", - }, - } - - actual = build_params( - datasource="foo", - dimensions=["bar"], - filter_pairs=filter_pairs, - granularity="all", - hours=24.0, - delay=3, - ) - actual["intervals"] = "" - diff = DeepDiff(expected, actual, ignore_order=True) - self.assertDictEqual({}, diff) - - @patch.object(PyDruid, "groupby", Mock(side_effect=OSError)) - def test_fetch_exception(self): - with self.assertRaises(DruidFetcherError): - _out = self.druid.fetch( - filter_keys=["assetId"], - filter_values=["5984175597303660107"], - dimensions=["ciStatus"], - datasource="customer-interaction-metrics", - aggregations={"count": aggregators.doublesum("count")}, - group_by=["timestamp", "ciStatus"], - hours=36, - pivot=Pivot( - index="timestamp", - columns=["ciStatus"], - value=["count"], - ), - ) -if __name__ == "__main__": - unittest.main() +@pytest.fixture() +def get_args(): + return { + "filter_keys": ["assetId"], + "filter_values": ["5984175597303660107"], + "dimensions": ["ciStatus"], + "datasource": "customer-interaction-metrics", + "aggregations": {"count": aggregators.doublesum("count")}, + "group_by": ["timestamp", "ciStatus"], + "pivot": Pivot( + index="timestamp", + columns=["ciStatus"], + value=["count"], + ), + "hours": 24, + } + + +@pytest.fixture() +def mock_query_fetch(mocker): + def _fetch(*_, **params): + interval = params["intervals"][0] + start, end = interval.split("/") + raw_df = pd.read_csv( + os.path.join(TESTS_DIR, "resources", "data", "raw_druid.csv"), index_col=0 + ) + return raw_df[raw_df["timestamp"].between(start, end)] + + mocker.patch.object(DruidFetcher, "_fetch", side_effect=_fetch) + + +@freeze_time("2024-02-22 15:30:00") +def test_chunked_fetch(get_args, mock_query_fetch): + fetcher = DruidFetcher(url="http://localhost:8888", endpoint="druid/v2/") + chunked_out = fetcher.chunked_fetch( + **get_args, + chunked_hours=1, + ) + full_out = fetcher.fetch(**get_args) + assert not chunked_out.empty + assert not full_out.empty + assert chunked_out.shape == full_out.shape + assert chunked_out.equals(full_out) + + +@freeze_time("2025-02-22 15:30:00") +def test_chunked_fetch_empty(get_args, mock_query_fetch): + fetcher = DruidFetcher(url="http://localhost:8888", endpoint="druid/v2/") + chunked_out = fetcher.chunked_fetch( + **get_args, + chunked_hours=1, + ) + full_out = fetcher.fetch(**get_args) + assert chunked_out.empty + assert full_out.empty + + +def test_chunked_fetch_err(get_args): + fetcher = DruidFetcher(url="http://localhost:8888", endpoint="druid/v2/") + with pytest.raises(ValueError): + fetcher.chunked_fetch( + **get_args, + chunked_hours=0, + ) diff --git a/tests/connectors/test_prometheus.py b/tests/connectors/test_prometheus.py index e1d2a958..57e3afd0 100644 --- a/tests/connectors/test_prometheus.py +++ b/tests/connectors/test_prometheus.py @@ -85,7 +85,6 @@ def _mock_mv(): "__name__": "namespace_app_rollouts_cpu_utilization", "app": "odl-graphql", "assetId": "723971233226699519", - "intuit_alert": "true", "namespace": "odl-odlgraphql-usw2-e2e", "numalogic": "true", "prometheus": "addon-metricset-ns/k8s-prometheus", @@ -107,7 +106,6 @@ def _mock_mv(): "__name__": "namespace_app_rollouts_cpu_utilization", "app": "odl-graphql", "assetId": "723971233226699519", - "intuit_alert": "true", "namespace": "odl-odlgraphql-usw2-e2e", "numalogic": "true", "prometheus": "addon-metricset-ns/k8s-prometheus", @@ -126,7 +124,6 @@ def _mock_mv(): "aiops_argorollouts": "true", "app": "odl-graphql", "assetId": "723971233226699519", - "intuit_alert": "true", "namespace": "odl-odlgraphql-usw2-e2e", "numalogic": "true", "prometheus": "addon-metricset-ns/k8s-prometheus", @@ -148,7 +145,6 @@ def _mock_mv(): "__name__": "namespace_app_rollouts_memory_utilization", "app": "odl-graphql", "assetId": "723971233226699519", - "intuit_alert": "true", "namespace": "odl-odlgraphql-usw2-e2e", "numalogic": "true", "prometheus": "addon-metricset-ns/k8s-prometheus", @@ -175,7 +171,6 @@ def _mock_uv(): "__name__": "namespace_app_rollouts_memory_utilization", "app": "odl-graphql", "assetId": "723971233226699519", - "intuit_alert": "true", "namespace": "odl-odlgraphql-usw2-e2e", "numalogic": "true", "prometheus": "addon-metricset-ns/k8s-prometheus", @@ -201,7 +196,6 @@ def _mock_no_metric(): "metric": { "app": "odl-graphql", "assetId": "723971233226699519", - "intuit_alert": "true", "namespace": "odl-odlgraphql-usw2-e2e", "numalogic": "true", "prometheus": "addon-metricset-ns/k8s-prometheus", diff --git a/tests/resources/data/raw_druid.csv 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